{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XBZWNJa2rJB0"
      },
      "source": [
        "## Actor-critic algorithms with safety constraints\n",
        "\n",
        "Here is a [better rendering](https://nbviewer.org/github/facebookresearch/Pearl/blob/main/tutorials/actor_critic_and_rc_safety_module/actor_critic_and_safe_actor_critic.ipynb) of this notebook on [nbviewer](https://nbviewer.org/).\n",
        "\n",
        "The class of actor-critic algorithms is known for its practical success. Pearl includes several popular algorithms in this class, such as PPO, DDPG, SAC, and TD3.\n",
        "\n",
        "In this tutorial, we will demonstrate how to use the TD3 algorithm with Pearl. Implementing other algorithms is just as simple; you only need to modify the Policy Learner to the appropriate algorithm.\n",
        "\n",
        "Pearl also supports safe training, which allows an algorithm designer to optimize a reward function while adhering to additional constraints. We will show you how to use safe training by instantiating the Safety Module with the Reward Constraint Safety Module.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8NNfwWXGvn_o",
        "outputId": "ee2d57ec-9d10-4b01-ccd0-ef72c6e58ba2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The autoreload extension is already loaded. To reload it, use:\n",
            "  %reload_ext autoreload\n"
          ]
        }
      ],
      "source": [
        "%load_ext autoreload\n",
        "%autoreload 2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YY0f_c_WLNGP"
      },
      "source": [
        "## Installation\n",
        "If you haven't installed Pearl, please make sure you install Pearl with the following cell. Otherwise, you can skip the cell below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1uLHbYlegKX-",
        "outputId": "5b727116-1eb9-4531-c43b-cdb4efc617ce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[33mWARNING: Skipping Pearl as it is not installed.\u001b[0m\u001b[33m\n",
            "\u001b[0mCloning into 'Pearl'...\n",
            "remote: Enumerating objects: 6412, done.\u001b[K\n",
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            "remote: Total 6412 (delta 1324), reused 1265 (delta 1177), pack-reused 4855 (from 1)\u001b[K\n",
            "Receiving objects: 100% (6412/6412), 55.14 MiB | 17.18 MiB/s, done.\n",
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            "/content/Pearl\n",
            "Processing /content/Pearl\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25hBuilding wheels for collected packages: Pearl\n",
            "  Building wheel for Pearl (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for Pearl: filename=Pearl-0.1.0-py3-none-any.whl size=217554 sha256=9a8e89e19485c74da616d55d16d33a8e5a021d8cd25dd41ef6087165db99e435\n",
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            "Successfully built Pearl\n",
            "Installing collected packages: glfw, farama-notifications, parameterized, gymnasium, ale-py, mujoco, Pearl\n",
            "Successfully installed Pearl-0.1.0 ale-py-0.10.1 farama-notifications-0.0.4 glfw-2.7.0 gymnasium-1.0.0 mujoco-3.2.4 parameterized-0.9.0\n",
            "/content\n"
          ]
        }
      ],
      "source": [
        "%pip uninstall Pearl -y\n",
        "%rm -rf Pearl\n",
        "!git clone https://github.com/facebookresearch/Pearl.git\n",
        "%cd Pearl\n",
        "%pip install .\n",
        "%cd .."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nCSJf8nALNGQ"
      },
      "source": [
        "## Import Modules"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "vcb70ZC_h3OA"
      },
      "outputs": [],
      "source": [
        "from pearl.utils.functional_utils.experimentation.set_seed import set_seed\n",
        "from pearl.replay_buffers import BasicReplayBuffer\n",
        "from pearl.utils.functional_utils.train_and_eval.online_learning import online_learning\n",
        "from pearl.pearl_agent import PearlAgent\n",
        "\n",
        "from pearl.user_envs.wrappers.gym_avg_torque_cost import GymAvgTorqueWrapper\n",
        "from pearl.utils.instantiations.environments.gym_environment import GymEnvironment\n",
        "import gymnasium as gym\n",
        "from pearl.policy_learners.sequential_decision_making.td3 import TD3\n",
        "from pearl.neural_networks.sequential_decision_making.actor_networks import VanillaContinuousActorNetwork\n",
        "from pearl.neural_networks.sequential_decision_making.q_value_networks import VanillaQValueNetwork\n",
        "from pearl.policy_learners.exploration_modules.common.normal_distribution_exploration import (\n",
        "    NormalDistributionExploration,\n",
        ")\n",
        "from pearl.safety_modules.reward_constrained_safety_module import (\n",
        "    RCSafetyModuleCostCriticContinuousAction,\n",
        ")\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "\n",
        "import torch\n",
        "import numpy as np\n",
        "\n",
        "set_seed(0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UYIoDAGSLNGR"
      },
      "source": [
        "Let's dive into the code. First, we will create a MuJoCo environment with an additional torque cost function. This environment is designed as a wrapper on top of Gym, so at each step, the environment returns the usual reward and an additional cost function. The cost function we use is $c(s,a)= ||a ||^2$, which represents the squared norm of the action. This cost function represents the average power of the action, and often, algorithm designers wish to restrict this quantity. In later sections, we will use the additional cost function to place additional constraints on the agent.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "td6gL3ZXisy6",
        "outputId": "1ec46289-9226-4e8a-9893-0fb5b5fc912c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gymnasium/envs/registration.py:517: DeprecationWarning: \u001b[33mWARN: The environment HalfCheetah-v4 is out of date. You should consider upgrading to version `v5`.\u001b[0m\n",
            "  logger.deprecation(\n",
            "/usr/local/lib/python3.10/dist-packages/gymnasium/spaces/box.py:235: UserWarning: \u001b[33mWARN: Box low's precision lowered by casting to float32, current low.dtype=float64\u001b[0m\n",
            "  gym.logger.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/gymnasium/spaces/box.py:305: UserWarning: \u001b[33mWARN: Box high's precision lowered by casting to float32, current high.dtype=float64\u001b[0m\n",
            "  gym.logger.warn(\n"
          ]
        }
      ],
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# create a HalfCheetah with an additional torque cost by using the GymAvgTorqueWrapper wrapper\n",
        "env = GymEnvironment(GymAvgTorqueWrapper(gym.make(\"HalfCheetah-v4\")))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FIXrgNvCh-1T"
      },
      "source": [
        "## TD3 algorithms in Pearl\n",
        "\n",
        "With the environment ready, we now test the performance of the TD3 algorithm. We set the Policy Learner to be TD3 and configure the replay buffer, but do not add any special safety module at this point.\n",
        "\n",
        "The following code demonstrates the performance of TD3 in the environment with the additional torque cost. As the learning progresses, we track and print both the cumulative reward and the cumulative cost."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6vfCofCSiNxQ",
        "outputId": "40b3dc18-c348-4451-9629-53762247c963"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pearl/policy_learners/exploration_modules/common/normal_distribution_exploration.py:54: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  low = torch.tensor(action_space.low).to(device)\n",
            "/usr/local/lib/python3.10/dist-packages/pearl/policy_learners/exploration_modules/common/normal_distribution_exploration.py:55: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  high = torch.tensor(action_space.high).to(device)\n",
            "/usr/local/lib/python3.10/dist-packages/pearl/neural_networks/sequential_decision_making/actor_networks.py:68: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  low = torch.tensor(action_space.low).to(device)\n",
            "/usr/local/lib/python3.10/dist-packages/pearl/neural_networks/sequential_decision_making/actor_networks.py:69: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  high = torch.tensor(action_space.high).to(device)\n",
            "/usr/local/lib/python3.10/dist-packages/pearl/policy_learners/sequential_decision_making/td3.py:156: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  low = torch.tensor(self._action_space.low, device=batch.device)\n",
            "/usr/local/lib/python3.10/dist-packages/pearl/policy_learners/sequential_decision_making/td3.py:157: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  high = torch.tensor(self._action_space.high, device=batch.device)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "episode 20, step 20000, agent=PearlAgent with TD3, BasicReplayBuffer, env=HalfCheetah-v4\n",
            "return: 204.48533967579715\n",
            "return_cost: 684.7595987766981\n",
            "episode 40, step 40000, agent=PearlAgent with TD3, BasicReplayBuffer, env=HalfCheetah-v4\n",
            "return: 758.1092373535794\n",
            "return_cost: 807.5460382699966\n",
            "episode 60, step 60000, agent=PearlAgent with TD3, BasicReplayBuffer, env=HalfCheetah-v4\n",
            "return: 1107.4033515322953\n",
            "return_cost: 828.62553191185\n",
            "episode 80, step 80000, agent=PearlAgent with TD3, BasicReplayBuffer, env=HalfCheetah-v4\n",
            "return: 893.773476209084\n",
            "return_cost: 785.0730047523975\n",
            "episode 100, step 100000, agent=PearlAgent with TD3, BasicReplayBuffer, env=HalfCheetah-v4\n",
            "return: 872.1770268091932\n",
            "return_cost: 793.2560573220253\n",
            "episode 120, step 120000, agent=PearlAgent with TD3, BasicReplayBuffer, env=HalfCheetah-v4\n",
            "return: 855.69622354905\n",
            "return_cost: 777.3230134248734\n",
            "episode 140, step 140000, agent=PearlAgent with TD3, BasicReplayBuffer, env=HalfCheetah-v4\n",
            "return: 926.8249725952337\n",
            "return_cost: 742.0758235752583\n",
            "episode 160, step 160000, agent=PearlAgent with TD3, BasicReplayBuffer, env=HalfCheetah-v4\n",
            "return: 983.4940501044039\n",
            "return_cost: 721.2012553811073\n",
            "episode 180, step 180000, agent=PearlAgent with TD3, BasicReplayBuffer, env=HalfCheetah-v4\n",
            "return: 1874.9885415658355\n",
            "return_cost: 780.8912108242512\n",
            "episode 200, step 200000, agent=PearlAgent with TD3, BasicReplayBuffer, env=HalfCheetah-v4\n",
            "return: 2308.0733566507697\n",
            "return_cost: 789.6627202630043\n"
          ]
        }
      ],
      "source": [
        "# setup TD3 algorithm\n",
        "td3_agent = PearlAgent(\n",
        "                    policy_learner=TD3(\n",
        "                        state_dim=env.observation_space.shape[0],\n",
        "                        action_space=env.action_space,\n",
        "                        actor_hidden_dims= [256, 256],\n",
        "                        critic_hidden_dims= [256, 256],\n",
        "                        training_rounds= 1,\n",
        "                        batch_size= 256,\n",
        "                        actor_network_type= VanillaContinuousActorNetwork,\n",
        "                        critic_network_type= VanillaQValueNetwork,\n",
        "                        actor_soft_update_tau= 0.005,\n",
        "                        critic_soft_update_tau= 0.005,\n",
        "                        actor_learning_rate= 1e-3,\n",
        "                        critic_learning_rate= 3e-4,\n",
        "                        discount_factor= 0.99,\n",
        "                        actor_update_freq= 2,\n",
        "                        actor_update_noise= 0.2,\n",
        "                        actor_update_noise_clip= 0.5,\n",
        "                        exploration_module=NormalDistributionExploration(\n",
        "                            mean=0.0,\n",
        "                            std_dev=0.1,\n",
        "                            ),\n",
        "                    ),\n",
        "                    replay_buffer=BasicReplayBuffer(\n",
        "                        capacity=100000,\n",
        "                    ),\n",
        "                    safety_module=None,\n",
        "                )\n",
        "\n",
        "# Run TD3 on the environment\n",
        "number_of_steps = 200000\n",
        "print_every_x_steps = 20000\n",
        "record_period = 1000\n",
        "\n",
        "td3_info = online_learning(\n",
        "    td3_agent,\n",
        "    env,\n",
        "    number_of_steps=number_of_steps,\n",
        "    print_every_x_steps=print_every_x_steps,\n",
        "    record_period=record_period,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WDxQIjW1tZFn"
      },
      "source": [
        "## TD3 with Reward Constrained safety module in Pearl\n",
        "\n",
        "The modular design of Pearl enables us to easily combine the TD3 algorithm with a safety module. One such generic safety module we developed is based on the Reward Constrained (RC) Policy Optimization (PO) framework [1].\n",
        "\n",
        "This approach is versatile and can be applied to any actor-critic algorithm. To try its Pearl implementation yourself, simply instantiate the Pearl agent safety module with the RC safety module.\n",
        "\n",
        "To integrate TD3 with the RC safety module, simply instantiate `PearlAgent` with TD3 as before, and set the `safety_module` parameter to `RCSafetyModuleCostCriticContinuousAction`. The following code demonstrates this.\n",
        "\n",
        "[1] Reward Constrained Policy Optimization, C. Tessler, D. Mankowitz, S. Mannor, 2019, https://arxiv.org/abs/1805.11074.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1WSyhE54tpTL",
        "outputId": "2632dc5b-91cb-438e-82c8-bfa872db46fd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "episode 20, step 20000, agent=PearlAgent with TD3, RCSafetyModuleCostCriticContinuousAction, BasicReplayBuffer, env=HalfCheetah-v4\n",
            "return: -11.054888365411898\n",
            "return_cost: 81.16531151242089\n",
            "episode 40, step 40000, agent=PearlAgent with TD3, RCSafetyModuleCostCriticContinuousAction, BasicReplayBuffer, env=HalfCheetah-v4\n",
            "return: 658.704802765511\n",
            "return_cost: 827.1898332089186\n",
            "episode 60, step 60000, agent=PearlAgent with TD3, RCSafetyModuleCostCriticContinuousAction, BasicReplayBuffer, env=HalfCheetah-v4\n",
            "return: 756.4797050401394\n",
            "return_cost: 405.57286664051935\n",
            "episode 80, step 80000, agent=PearlAgent with TD3, RCSafetyModuleCostCriticContinuousAction, BasicReplayBuffer, env=HalfCheetah-v4\n",
            "return: 2565.197494857013\n",
            "return_cost: 612.0564894992858\n",
            "episode 100, step 100000, agent=PearlAgent with TD3, RCSafetyModuleCostCriticContinuousAction, BasicReplayBuffer, env=HalfCheetah-v4\n",
            "return: 3533.8661237433553\n",
            "return_cost: 599.428830139339\n",
            "episode 120, step 120000, agent=PearlAgent with TD3, RCSafetyModuleCostCriticContinuousAction, BasicReplayBuffer, env=HalfCheetah-v4\n",
            "return: 3900.335495904088\n",
            "return_cost: 512.5770066995174\n",
            "episode 140, step 140000, agent=PearlAgent with TD3, RCSafetyModuleCostCriticContinuousAction, BasicReplayBuffer, env=HalfCheetah-v4\n",
            "return: 4077.7133386665955\n",
            "return_cost: 402.4540570154786\n",
            "episode 160, step 160000, agent=PearlAgent with TD3, RCSafetyModuleCostCriticContinuousAction, BasicReplayBuffer, env=HalfCheetah-v4\n",
            "return: 4583.259469367564\n",
            "return_cost: 418.9337814170867\n",
            "episode 180, step 180000, agent=PearlAgent with TD3, RCSafetyModuleCostCriticContinuousAction, BasicReplayBuffer, env=HalfCheetah-v4\n",
            "return: 4885.99183803983\n",
            "return_cost: 395.03990103676915\n",
            "episode 200, step 200000, agent=PearlAgent with TD3, RCSafetyModuleCostCriticContinuousAction, BasicReplayBuffer, env=HalfCheetah-v4\n",
            "return: 5254.420082072727\n",
            "return_cost: 378.0495346598327\n"
          ]
        }
      ],
      "source": [
        "# setup RCTD3 algorithm, TD3 with reward constraint safety module\n",
        "rctd3_agent = PearlAgent(\n",
        "                    policy_learner=TD3(\n",
        "                        state_dim=env.observation_space.shape[0],\n",
        "                        action_space=env.action_space,\n",
        "                        actor_hidden_dims= [256, 256],\n",
        "                        critic_hidden_dims= [256, 256],\n",
        "                        training_rounds= 1,\n",
        "                        batch_size= 256,\n",
        "                        actor_network_type= VanillaContinuousActorNetwork,\n",
        "                        critic_network_type= VanillaQValueNetwork,\n",
        "                        actor_soft_update_tau= 0.005,\n",
        "                        critic_soft_update_tau= 0.005,\n",
        "                        actor_learning_rate= 1e-3,\n",
        "                        critic_learning_rate= 3e-4,\n",
        "                        discount_factor= 0.99,\n",
        "                        actor_update_freq= 2,\n",
        "                        actor_update_noise= 0.2,\n",
        "                        actor_update_noise_clip= 0.5,\n",
        "                        exploration_module=NormalDistributionExploration(\n",
        "                            mean=0.0,\n",
        "                            std_dev=0.1,\n",
        "                            ),\n",
        "                    ),\n",
        "                    replay_buffer=BasicReplayBuffer(\n",
        "                        capacity=100000,\n",
        "                    ),\n",
        "                    safety_module=RCSafetyModuleCostCriticContinuousAction(\n",
        "                        state_dim=env.observation_space.shape[0],\n",
        "                        action_space=env.action_space,\n",
        "                        critic_hidden_dims= [256, 256],\n",
        "                        constraint_value=0.4,\n",
        "                        lambda_constraint_ub_value=200.0,\n",
        "                        lr_lambda=1e-3\n",
        "                    ),\n",
        "                )\n",
        "\n",
        "# Run RCTD3 on the environment\n",
        "number_of_steps = 200000\n",
        "print_every_x_steps = 20000\n",
        "record_period = 1000\n",
        "\n",
        "rctd3_info = online_learning(\n",
        "    rctd3_agent,\n",
        "    env,\n",
        "    number_of_steps=number_of_steps,\n",
        "    print_every_x_steps=print_every_x_steps,\n",
        "    record_period=record_period,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RR5SQJlvcTrL"
      },
      "source": [
        "## More on RC-PO and Its Pearl Implementation\n",
        "\n",
        "Next we elaborate on the RC-PO framework and its Pearl implementation. The RC-PO framework is based on the Lagrangian formulation of Constraint MDPs (CMDP). Effectively, it translates the solution of a CMDP to a min-max optimization problem. The min-player solves an MDP with a modifed reward of\n",
        "$$\n",
        "r_\\lambda(s,a) = r(s,a) + \\lambda (c(s,a)-\\alpha),\n",
        "$$\n",
        "where $r(s,a)$ and $c(s,a)$ are the immediate reward and cost, $\\alpha$ is the constraint, and $\\lambda$ is the Lagrange multiplier. On the other hand, the max-player maximizes $\\lambda$, and increases it as long the agent does not satisfy the constraint.\n",
        "\n",
        "In Pearl, when the safety module is instantiated with RC safety, the baseline actor optimizes the effective reward $r_\\lambda(s,a)$ while updating the Lagrange multiplier."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VA1xdInBvQE2"
      },
      "source": [
        "## Comparing the results\n",
        "\n",
        "Let us now compare the results. As we can see, the cumulative cost of the RCTD3 agent is much better controlled than that of the TD3 agent. Additionally, the cumulative reward of the RCTD3 agent is higher in this particular problem, which may be due to the extra regularization provided by the RC module. This is not always the case, but it is possible in certain situations.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "slEdjytvvU8k",
        "outputId": "66abf2d6-8e04-4e98-ffe1-4538d407ce2d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Plot the cummulative return and cummulative cost\n",
        "steps = np.arange(200) * record_period\n",
        "plt.plot(steps, td3_info[\"return\"], label='TD3')\n",
        "plt.plot(steps, rctd3_info[\"return\"], label='RCTD3')\n",
        "plt.xlabel('Time step')\n",
        "plt.ylabel('Cummulative return')\n",
        "plt.title(\"Cummulative return\")\n",
        "plt.legend()\n",
        "plt.show()\n",
        "# Create the second plot\n",
        "plt.plot(steps, td3_info[\"return_cost\"], label='TD3')\n",
        "plt.plot(steps, rctd3_info[\"return_cost\"], label='RCTD3')\n",
        "plt.xlabel('Time step')\n",
        "plt.ylabel('Cummulative cost')\n",
        "plt.title(\"Cummulative cost\")\n",
        "plt.legend()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pm18iQ_2LNGS"
      },
      "source": [
        "## Summary\n",
        "In this tutorial, we showed how to use the TD3 algorithm and its safe variant, we refer as RCTD3, in Pearl. Further, we elaborated on the RC framework, and empirically tested the performance of TD3 and RCTD3.\n"
      ]
    },
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        "id": "LaOXrDCtzPKD"
      },
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      "source": []
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